def amsub_hdf(fnames, metadata_only=False, chans=None):
''' Read AMSU-B hdf data products.
All GeoIPS 2.0 readers read data into xarray Datasets - a separate
dataset for each shape/resolution of data - and contain standard metadata information.
Args:
fnames (list): List of strings, full paths to files
metadata_only (Optional[bool]):
* DEFAULT False
* return before actually reading data if True
chans (Optional[list of str]):
* NOT IMPLEMENTED
* DEFAULT None (include all channels)
* List of desired channels (skip unneeded variables as needed)
Returns:
list of xarray.Datasets: list of xarray.Dataset objects with required Variables and Attributes:
* See geoips2/docs :doc:`xarray_standards`
'''
import os
from datetime import datetime
import numpy as np
import pandas as pd
import xarray as xr
#from IPython import embed as shell
#fname='NPR.MHOP.NP.D20154.S1406.E1553.B5833031.NS'
fname = fnames[0]
LOG.info('Reading file %s', fname)
# check for right input AMSU-B/MHS data file
data_name = os.path.basename(fname).split('_')[-1].split('.')[-1]
if data_name != 'NS':
print('Warning: wrong AMSU-B/MHS data type: data_type=', data_name)
raise
if ('NRP' and 'MHOP') in os.path.basename(fname):
print('found a AMSU-B/MHS hdf file')
else:
print('not a AMSU-B/MHS hdf file: skip it')
raise
''' ------ Notes ------
Read AMSU-B hdf files for 5 chan antenna temperature (AT) and asscoaited EDRs
Then, transform these ATs and fields into xarray framework for GEOIPS2
( AT will be corrected into brightness temperature (TB) later)
Input Parameters:
fname (str): input file name. require a full path
Returns:
xarray.Dataset with required Variables and Attributes:
Variables:
AMSUB vars:
'latitude', 'longitude', 'Ch1', 'Ch2', 'Ch3', 'Ch4','Ch4',
'RR', 'Snow','SWE','IWP','SFR' 'sfcType', 'time_scan'
Attibutes:
'source_name', 'platform_name', 'data_provider',
'interpolation_radius_of_influence','start_datetime', 'end_datetime'
'''
SData_ID = SD(fname, SDC.READ)
VData_ID = HDF(fname, HC.READ).vstart()
# get scan time info
year = VData_ID.attach('ScanTime_year')[:]
month = VData_ID.attach('ScanTime_month')[:]
day = VData_ID.attach('ScanTime_dom')[:]
hour = VData_ID.attach('ScanTime_hour')[:]
minute = VData_ID.attach('ScanTime_minute')[:]
second = VData_ID.attach('ScanTime_second')[:]
jday = VData_ID.attach('ScanTime_doy')[:]
# get EDRs
lat = SData_ID.select('Latitude').get()
lon = SData_ID.select('Longitude').get()
RR = SData_ID.select('RR').get()
Snow = SData_ID.select('Snow').get()
IWP = SData_ID.select('IWP').get()
SWE = SData_ID.select('SWE').get()
SFR = SData_ID.select('SFR').get()
Sfc_type = SData_ID.select('Sfc_type').get()
Chan1_AT = SData_ID.select('Chan1_AT').get()
Chan2_AT = SData_ID.select('Chan2_AT').get()
Chan3_AT = SData_ID.select('Chan3_AT').get()
Chan4_AT = SData_ID.select('Chan4_AT').get()
Chan5_AT = SData_ID.select('Chan5_AT').get()
SZ_angle = SData_ID.select('SZ_angle').get()
LZ_angle = SData_ID.select('LZ_angle').get()
# Min, Max, and scale factors (using hdfview)
AT_min = 75.0
AT_max = 325.0
RR_min = 0.0
RR_max = 30.0
Snow_min = 0.0
Snow_max = 100.0
IWP_min = 0.0
IWP_max = 3.0
SWE_min = 0.0
SWE_max = 30.0
SFR_min = 0.03
SFR_max = 5.0
AT_scale = 100.0
RR_scale = 10.0
Snow_scale = 1.0
IWP_scale = 100.0
SWE_scale = 100.0
SFR_scale = 100.0
# Adjust the values with scale factors
RR = RR * RR_scale
Snow = Snow / Snow_scale
IWP = IWP / IWP_scale
SWE = SWE / SWE_scale
SFR = SFR / SFR_scale
Chan1_AT = Chan1_AT / AT_scale
Chan2_AT = Chan2_AT / AT_scale
Chan3_AT = Chan3_AT / AT_scale
Chan4_AT = Chan4_AT / AT_scale
Chan5_AT = Chan5_AT / AT_scale
SData_ID.end() # close input file
VData_ID.end() # close input file
# -------- Apply the GEOIPS2 framework in XARRAY data frame ----------
LOG.info('Making full dataframe')
# setup the timestamp in datetime64 format
npix = RR.shape[1] # pixels per scan
nscan = RR.shape[0] # total scans of this file
time_scan = np.zeros((nscan, npix))
for i in range(nscan):
time_scan[i:] = '%04d%03d%02d%02d' % (year[i][0], jday[i][0],
hour[i][0], minute[i][0])
# ------ setup xarray variables ------
#namelist_amsub = ['latitude', 'longitude', 'Chan1_AT', 'Chan2_AT', 'Chan3_AT','Chan4_AT','Chan5_AT',
# 'RR','Snow','IWP','SWE','SFR','Sfc_type','timestamp']
# setup amsub xarray
xarray_amsub = xr.Dataset()
xarray_amsub['latitude'] = xr.DataArray(lat)
xarray_amsub['longitude'] = xr.DataArray(lon)
xarray_amsub['Chan1_AT'] = xr.DataArray(Chan1_AT)
xarray_amsub['Chan2_AT'] = xr.DataArray(Chan2_AT)
xarray_amsub['Chan3_AT'] = xr.DataArray(Chan3_AT)
xarray_amsub['Chan4_AT'] = xr.DataArray(Chan4_AT)
xarray_amsub['Chan5_AT'] = xr.DataArray(Chan5_AT)
xarray_amsub['RR'] = xr.DataArray(RR)
xarray_amsub['Snow'] = xr.DataArray(Snow)
xarray_amsub['IWP'] = xr.DataArray(IWP)
xarray_amsub['SWE'] = xr.DataArray(SWE)
xarray_amsub['SFR'] = xr.DataArray(SFR)
xarray_amsub['sfcType'] = xr.DataArray(Sfc_type)
xarray_amsub['timestamp'] = xr.DataArray(
pd.DataFrame(time_scan).astype(int).apply(pd.to_datetime,
format='%Y%j%H%M'))
# setup attributes
# satID and start_end time from input filename (M3 --> METOP-3 ???)
sat_id = os.path.basename(fname).split('.')[2]
if sat_id == 'NP':
satid = 'NOAA-19'
elif sat_id == 'NN':
satid = 'NOAA-18'
elif sat_id == 'M1':
satid = 'METOP-B'
elif sat_id == 'M2':
satid = 'METOP-A'
elif sat_id == 'M3':
satid = 'METOP-C'
yr = os.path.basename(fname).split('.')[3][1:3]
jd = os.path.basename(fname).split('.')[3][3:6]
hr = os.path.basename(fname).split('.')[4][1:3]
mi = os.path.basename(fname).split('.')[4][3:6]
start_time = '%02d%03d%02d%02d' % (int(yr), int(jd), int(hr), int(mi))
hr = os.path.basename(fname).split('.')[5][1:3]
mi = os.path.basename(fname).split('.')[5][3:6]
end_time = '%02d%03d%02d%02d' % (int(yr), int(jd), int(hr), int(mi))
# add attributes to xarray
xarray_amsub.attrs['start_datetime'] = datetime.strptime(
start_time, '%y%j%H%M')
xarray_amsub.attrs['end_datetime'] = datetime.strptime(
end_time, '%y%j%H%M')
xarray_amsub.attrs['source_name'] = 'amsub'
xarray_amsub.attrs['platform_name'] = satid
xarray_amsub.attrs['data_provider'] = 'NESDIS'
# MTIFs need to be "prettier" for PMW products, so 2km resolution for final image
# xarray_amsub.attrs['sample_distance_km'] = 15
xarray_amsub.attrs['sample_distance_km'] = 2
xarray_amsub.attrs['interpolation_radius_of_influence'] = 30000
return [xarray_amsub]
